{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from timeit import default_timer as timer\n",
    "# needed to see images\n",
    "from IPython.display import display, Image\n",
    "import pickle\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "\n",
    "# needed for plotting\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "current_palette = sns.color_palette()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.12.0-rc1'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using TensorFlow V.12\n",
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getkey(item):\n",
    "    fpr, tpr, _ = roc_curve(test_labels.ravel(), test_preds[item])\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    return roc_auc\n",
    "\n",
    "def plotROC(testlabels, test_preds):\n",
    "    classifiers = list(test_preds.keys())\n",
    "\n",
    "    # Plot all ROC curves\n",
    "    plt.figure(figsize=(15,9))\n",
    "    for i, clf in zip(range(len(classifiers)), sorted(classifiers, key=getkey, reverse=True)):\n",
    "        fpr, tpr, _ = roc_curve(testlabels.ravel(), test_preds[clf] )\n",
    "        roc_auc = auc(fpr, tpr)\n",
    "        plt.plot(fpr, tpr,\n",
    "                 label='ROC curve '+ clf +  ' (area = {0:0.4f})'\n",
    "                       ''.format(roc_auc), linestyle='-', linewidth=2)\n",
    "\n",
    "\n",
    "    plt.plot([0, 1], [0, 1], 'k--', lw=2)\n",
    "    plt.xlim([-0.1, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Comparison of multiclass ROC curves')\n",
    "    plt.legend(loc=\"lower right\", fontsize=14)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = pickle.load(open('193_features.p', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8730, 14)\n",
      "Index(['shape', 'label', 'fold', 'dog_bark', 'air_cond', 'jackhamm', 'siren',\n",
      "       'engine_i', 'street_m', 'car_horn', 'gun_shot', 'children', 'drilling',\n",
      "       'sample'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(data.shape)\n",
    "print(data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working dataframe's shape: (8730, 194)\n"
     ]
    }
   ],
   "source": [
    "s = list(data['sample'])\n",
    "s = pd.DataFrame(s)\n",
    "data_cols = s.columns\n",
    "s['label'] = data['label']\n",
    "print('working dataframe\\'s shape:', s.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train shape (6984, 194)\n",
      "test  shape (1746, 194)\n",
      "common factors: 1, 2, 3, 6, 9, 18, 97, 194, 291, 582, 873, 1746\n"
     ]
    }
   ],
   "source": [
    "# train test split\n",
    "test_preds = {}\n",
    "train = s[0:6984]\n",
    "test = s[6984:]\n",
    "LB = LabelBinarizer().fit(train['label'])\n",
    "test_labels = LB.transform(test['label'])\n",
    "\n",
    "# print shapes\n",
    "print('train shape {}\\ntest  shape {}\\ncommon factors: 1, 2, 3, 6, 9, 18, 97, 194, 291, 582, 873, 1746'.format(train.shape, test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "del data, s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def accuracy(predictions, labels):\n",
    "    return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))\n",
    "            / predictions.shape[0])\n",
    "\n",
    "def weight_variable(shape):\n",
    "    initial = tf.truncated_normal(shape, stddev=0.01)\n",
    "    return tf.Variable(initial)\n",
    "\n",
    "def bias_variable(shape):\n",
    "    initial = tf.constant(0.1, shape=shape)\n",
    "    return tf.Variable(initial)\n",
    "\n",
    "def test_accuracy(session, test_data=test, during = True):\n",
    "    test_data.reset_index(inplace=True, drop=True)\n",
    "    epoch_pred = session.run(prediction, feed_dict={tf_data : test_data.loc[0:check_size-1,data_cols], keep_prob : 1.0})\n",
    "    for i in range(check_size, test_data.shape[0], check_size):\n",
    "        epoch_pred = np.concatenate([epoch_pred, session.run(prediction, \n",
    "                                    feed_dict={tf_data : test_data.loc[i:i+check_size-1,data_cols], keep_prob : 1.0})], axis=0)\n",
    "    if during:\n",
    "        return accuracy(epoch_pred, test_labels)\n",
    "    else:\n",
    "        return epoch_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Run Session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "num_labels = 10\n",
    "batch_size = 97\n",
    "acc_over_time = {}\n",
    "def Run_Session(num_epochs, name, k_prob=1.0, mute=False, record=False):\n",
    "    global train\n",
    "    \n",
    "    start = timer()\n",
    "    with tf.Session(graph=graph) as session:\n",
    "        if record:\n",
    "            merged = tf.merge_all_summaries()  \n",
    "            writer = tf.train.SummaryWriter(\"/tmp/tensorflowlogs\", session.graph)\n",
    "        #tf.initialize_all_variables().run()\n",
    "        tf.global_variables_initializer().run()\n",
    "\n",
    "        print(\"Initialized\")\n",
    "        accu = []\n",
    "        \n",
    "        for epoch in range(num_epochs):\n",
    "            \n",
    "            # get batch\n",
    "            train_batch = train.sample(batch_size)\n",
    "            \n",
    "            t_d = train_batch[data_cols]\n",
    "            t_l = LB.transform(train_batch['label'])\n",
    "            \n",
    "            # make feed dict\n",
    "            feed_dict = { tf_data : t_d, train_labels : t_l, keep_prob : k_prob}\n",
    "            \n",
    "            # run model on batch\n",
    "            _, l, predictions = session.run([optimizer, loss, prediction], feed_dict=feed_dict)\n",
    "            \n",
    "            # mid model accuracy checks \n",
    "            if (epoch % 1000 == 0) and not mute:\n",
    "                print(\"\\tMinibatch loss at epoch {}: {}\".format(epoch, l))\n",
    "                print(\"\\tMinibatch accuracy: {:.1f}\".format(accuracy(predictions, t_l)))\n",
    "            if (epoch % 5000 == 0) and not mute:\n",
    "                print(\"Test accuracy: {:.1f}\".format(test_accuracy(session, during=True)))\n",
    "            if (epoch % 1000 == 0) and not mute:\n",
    "                accu.append(tuple([epoch, test_accuracy(session, during=True)]))\n",
    "                \n",
    "        # record accuracy and predictions\n",
    "        test_preds[name] = test_accuracy(session, during=False)\n",
    "        print(\"Final Test accuracy: {:.1f}\".format(accuracy(test_preds[name], test_labels)))\n",
    "        end = timer()\n",
    "        test_preds[name] = test_preds[name].ravel()\n",
    "        acc_over_time[name] = accu\n",
    "        print(\"time taken: {0} minutes {1:.1f} seconds\".format((end - start)//60, (end - start)%60))\n",
    "        #tf.train.export_meta_graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## RNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Basic RNN model made\n"
     ]
    }
   ],
   "source": [
    "# constants\n",
    "num_labels = 10\n",
    "batch_size = 97\n",
    "check_size = 97\n",
    "feature_size = 193\n",
    "n_hidden = 200\n",
    "beta = 0.04\n",
    "\n",
    "graph = tf.Graph()\n",
    "with graph.as_default():\n",
    "    # placeholders\n",
    "    tf_data = tf.placeholder(tf.float32, shape=[None, feature_size])\n",
    "    train_labels = tf.placeholder(tf.float32, shape=[None, num_labels])\n",
    "    keep_prob = tf.placeholder(tf.float32)\n",
    "    \n",
    "    # weights and biases\n",
    "    layer1_weights = weight_variable([feature_size*n_hidden, num_labels])\n",
    "    layer1_biases = bias_variable([num_labels])\n",
    "    \n",
    "    # model\n",
    "    def model(data, proba=1.0):\n",
    "        # Init RNN cell # tensorflow seems to take care of not reinitializing each time model is called\n",
    "        cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden, state_is_tuple=True)\n",
    "        # run rnn cell\n",
    "        layer1, _istate = tf.nn.dynamic_rnn(cell, data, dtype=tf.float32)\n",
    "        # reshape for output layer.\n",
    "        layer1 = tf.reshape(layer1, shape=[batch_size, feature_size*n_hidden])\n",
    "        layer2 = tf.nn.dropout(layer1, proba)\n",
    "        return tf.matmul(layer2, layer1_weights) + layer1_biases\n",
    "\n",
    "    # Training computation.\n",
    "    logits = model(tf.expand_dims(tf_data, -1), keep_prob)\n",
    "    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits, train_labels) +\n",
    "                         beta*tf.nn.l2_loss(layer1_weights) +\n",
    "                         beta*tf.nn.l2_loss(layer1_biases))\n",
    "\n",
    "    # Optimizer.\n",
    "    optimizer = tf.train.AdamOptimizer(1e-4).minimize(loss)\n",
    "\n",
    "    # Predictions for the training, validation, and test data.\n",
    "    # !!!! as this model does not allow calling model more than once we will need to get test predictions\n",
    "    #      by running the test data through the same prediction op as the train data. !!!\n",
    "    # !!! make note to not run optimizer on same function call !!!\n",
    "    prediction = tf.nn.softmax(logits) \n",
    "    print('Basic RNN model made')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "\tMinibatch loss at epoch 0: 3.091226816177368\n",
      "\tMinibatch accuracy: 12.4\n",
      "Test accuracy: 10.4\n",
      "\tMinibatch loss at epoch 1000: 0.8719662427902222\n",
      "\tMinibatch accuracy: 77.3\n",
      "\tMinibatch loss at epoch 2000: 0.4672536253929138\n",
      "\tMinibatch accuracy: 88.7\n",
      "\tMinibatch loss at epoch 3000: 0.3073025941848755\n",
      "\tMinibatch accuracy: 95.9\n",
      "\tMinibatch loss at epoch 4000: 0.2603219151496887\n",
      "\tMinibatch accuracy: 96.9\n",
      "Final Test accuracy: 51.7\n",
      "time taken: 107.0 minutes 45.6 seconds\n"
     ]
    }
   ],
   "source": [
    "Run_Session(5000, 'RNN', .2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DeepNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Basic DeepNN model made\n"
     ]
    }
   ],
   "source": [
    "# constants\n",
    "num_labels = 10\n",
    "\n",
    "batch_size = 97\n",
    "check_size = 582\n",
    "feature_size = 193\n",
    "\n",
    "n_hidden1 = 400\n",
    "n_hidden2 = 500\n",
    "n_hidden3 = 400\n",
    "\n",
    "beta = 0.01\n",
    "\n",
    "graph = tf.Graph()\n",
    "with graph.as_default():\n",
    "    # placeholders\n",
    "    tf_data = tf.placeholder(tf.float32, shape=[None, feature_size])\n",
    "    train_labels = tf.placeholder(tf.float32, shape=[None, num_labels])\n",
    "    #test_data = tf.placeholder(tf.float32, shape=[batch_size, feature_size, 1])\n",
    "    keep_prob = tf.placeholder(tf.float32)\n",
    "    \n",
    "    # weights and biases\n",
    "    layer1_weights = weight_variable([feature_size, n_hidden1])\n",
    "    layer1_biases = bias_variable([n_hidden1])\n",
    "    layer2_weights = weight_variable([n_hidden1, n_hidden2])\n",
    "    layer2_biases = bias_variable([n_hidden2])\n",
    "    layer3_weights = weight_variable([n_hidden2, n_hidden3])\n",
    "    layer3_biases = bias_variable([n_hidden3])\n",
    "    layer4_weights = weight_variable([n_hidden3, num_labels])\n",
    "    layer4_biases = bias_variable([num_labels])\n",
    "\n",
    "    # model\n",
    "    def model(data, proba=1.0):\n",
    "        layer1 = tf.nn.relu(tf.matmul(data, layer1_weights) + layer1_biases)\n",
    "        layer1 = tf.nn.dropout(layer1, proba)\n",
    "        \n",
    "        layer2 = tf.nn.relu(tf.matmul(layer1, layer2_weights) + layer2_biases)\n",
    "        layer2 = tf.nn.dropout(layer2, proba)\n",
    "        \n",
    "        layer3 = tf.nn.relu(tf.matmul(layer2, layer3_weights) + layer3_biases)\n",
    "        layer3 = tf.nn.dropout(layer3, proba)\n",
    "        \n",
    "        return tf.matmul(layer3, layer4_weights) + layer4_biases\n",
    "\n",
    "    # Training computation.\n",
    "    logits = model(tf_data, keep_prob)\n",
    "    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits, train_labels) +\n",
    "                         beta*tf.nn.l2_loss(layer1_weights) +\n",
    "                         beta*tf.nn.l2_loss(layer1_biases) +\n",
    "                         beta*tf.nn.l2_loss(layer2_weights) +\n",
    "                         beta*tf.nn.l2_loss(layer2_biases) +\n",
    "                         beta*tf.nn.l2_loss(layer3_weights) +\n",
    "                         beta*tf.nn.l2_loss(layer3_biases) +\n",
    "                         beta*tf.nn.l2_loss(layer4_weights) +\n",
    "                         beta*tf.nn.l2_loss(layer4_biases))\n",
    "\n",
    "    # Optimizer.\n",
    "    optimizer = tf.train.AdamOptimizer(1e-4).minimize(loss)\n",
    "\n",
    "    # Predictions for the training, validation, and test data.\n",
    "    # !!!! as this RNN model does not allow calling model more than once we will need to get test predictions\n",
    "    #      by running the test data through the same prediction op as the train data. !!!\n",
    "    # !!! make note to not run optimizer on same function call !!!\n",
    "    prediction = tf.nn.softmax(logits)\n",
    "    ### test_prediction = tf.nn.softmax(model(test_data))  \n",
    "    print('Basic DeepNN model made')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "\tMinibatch loss at epoch 0: 3.636944055557251\n",
      "\tMinibatch accuracy: 12.4\n",
      "Test accuracy: 16.2\n",
      "\tMinibatch loss at epoch 1000: 1.2090295553207397\n",
      "\tMinibatch accuracy: 67.0\n",
      "\tMinibatch loss at epoch 2000: 1.073646903038025\n",
      "\tMinibatch accuracy: 80.4\n",
      "\tMinibatch loss at epoch 3000: 0.7408252358436584\n",
      "\tMinibatch accuracy: 85.6\n",
      "\tMinibatch loss at epoch 4000: 0.7549278736114502\n",
      "\tMinibatch accuracy: 83.5\n",
      "\tMinibatch loss at epoch 5000: 0.708027184009552\n",
      "\tMinibatch accuracy: 84.5\n",
      "Test accuracy: 62.1\n",
      "\tMinibatch loss at epoch 6000: 0.7181379199028015\n",
      "\tMinibatch accuracy: 81.4\n",
      "\tMinibatch loss at epoch 7000: 0.7154595255851746\n",
      "\tMinibatch accuracy: 86.6\n",
      "\tMinibatch loss at epoch 8000: 0.49731531739234924\n",
      "\tMinibatch accuracy: 93.8\n",
      "\tMinibatch loss at epoch 9000: 0.5555043816566467\n",
      "\tMinibatch accuracy: 91.8\n",
      "\tMinibatch loss at epoch 10000: 0.7170403003692627\n",
      "\tMinibatch accuracy: 88.7\n",
      "Test accuracy: 62.9\n",
      "\tMinibatch loss at epoch 11000: 0.5773162245750427\n",
      "\tMinibatch accuracy: 88.7\n",
      "\tMinibatch loss at epoch 12000: 0.616878867149353\n",
      "\tMinibatch accuracy: 87.6\n",
      "\tMinibatch loss at epoch 13000: 0.567261278629303\n",
      "\tMinibatch accuracy: 93.8\n",
      "\tMinibatch loss at epoch 14000: 0.47560736536979675\n",
      "\tMinibatch accuracy: 95.9\n",
      "\tMinibatch loss at epoch 15000: 0.5731883645057678\n",
      "\tMinibatch accuracy: 90.7\n",
      "Test accuracy: 62.8\n",
      "\tMinibatch loss at epoch 16000: 0.529100239276886\n",
      "\tMinibatch accuracy: 90.7\n",
      "\tMinibatch loss at epoch 17000: 0.5827775001525879\n",
      "\tMinibatch accuracy: 91.8\n",
      "\tMinibatch loss at epoch 18000: 0.47068318724632263\n",
      "\tMinibatch accuracy: 91.8\n",
      "\tMinibatch loss at epoch 19000: 0.4940539598464966\n",
      "\tMinibatch accuracy: 95.9\n",
      "Final Test accuracy: 62.4\n",
      "time taken: 32.0 minutes 10.9 seconds\n"
     ]
    }
   ],
   "source": [
    "Run_Session(20000, 'DeepNN', .2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Basic CNN model made\n"
     ]
    }
   ],
   "source": [
    "# single layer CNN\n",
    "num_labels = 10\n",
    "\n",
    "batch_size = 97\n",
    "check_size = 582\n",
    "feature_size = 193\n",
    "patch_size = 5\n",
    "num_channels = 1\n",
    "depth1 = 32\n",
    "\n",
    "num_hidden = 1050\n",
    "\n",
    "beta = 0.01\n",
    "\n",
    "graph = tf.Graph()\n",
    "with graph.as_default():\n",
    "    \n",
    "    tf_data = tf.placeholder(tf.float32, shape=(None, feature_size))\n",
    "    train_labels = tf.placeholder(tf.float32, shape=(None, num_labels))\n",
    "    #tf_test_dataset = tf.placeholder(tf.float32, shape=(check_size, image_size, image_size, num_channels))\n",
    "\n",
    "    # Variables.\n",
    "    layer1_weights = weight_variable([1, patch_size, 1, depth1])\n",
    "    layer1_biases = bias_variable([depth1])\n",
    "    layer2_weights = weight_variable([(feature_size//2 + 1)* depth1, num_hidden])\n",
    "    layer2_biases = bias_variable([num_hidden])\n",
    "    layer3_weights = weight_variable([num_hidden, num_labels])\n",
    "    layer3_biases = bias_variable([num_labels])\n",
    "\n",
    "    keep_prob = tf.placeholder(tf.float32)\n",
    "    # Model with dropout\n",
    "    def model(data, proba=keep_prob):\n",
    "        # Convolution\n",
    "        conv1 = tf.nn.conv2d(data, layer1_weights, [1, 1, 2, 1] , padding='SAME') + layer1_biases\n",
    "        pooled1 = tf.nn.max_pool(tf.nn.relu(conv1), ksize=[1, 1, 2, 1],strides=[1, 1, 1, 1], padding='SAME')\n",
    "\n",
    "        # Fully Connected Layer\n",
    "        shape = pooled1.get_shape().as_list()\n",
    "        reshape = tf.reshape(pooled1, [-1, shape[1] * shape[2] * shape[3]])\n",
    "        full2 = tf.nn.relu(tf.matmul(reshape, layer2_weights) + layer2_biases)\n",
    "\n",
    "        # Dropout\n",
    "        full2 = tf.nn.dropout(full2, proba)\n",
    "        \n",
    "        return tf.matmul(full2, layer3_weights) + layer3_biases\n",
    "\n",
    "    # Training computation.\n",
    "    logits = model(tf.expand_dims(tf.expand_dims(tf_data, [-1]), 1), keep_prob)\n",
    "    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits, train_labels) +\n",
    "                         beta*tf.nn.l2_loss(layer1_weights) +\n",
    "                         beta*tf.nn.l2_loss(layer1_biases) +\n",
    "                         beta*tf.nn.l2_loss(layer2_weights) +\n",
    "                         beta*tf.nn.l2_loss(layer2_biases) +\n",
    "                         beta*tf.nn.l2_loss(layer3_weights) +\n",
    "                         beta*tf.nn.l2_loss(layer3_biases))\n",
    "\n",
    "    # Optimizer.\n",
    "    optimizer = tf.train.AdamOptimizer(1e-4).minimize(loss)\n",
    "\n",
    "    # Predictions for the training, validation, and test data.\n",
    "    prediction = tf.nn.softmax(logits)\n",
    "    #test_prediction = tf.nn.softmax(model(tf_test_dataset,1.0))  \n",
    "    print('Basic CNN model made')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "\tMinibatch loss at epoch 0: 3.636146068572998\n",
      "\tMinibatch accuracy: 8.2\n",
      "Test accuracy: 12.6\n",
      "\tMinibatch loss at epoch 1000: 0.9397976398468018\n",
      "\tMinibatch accuracy: 77.3\n",
      "\tMinibatch loss at epoch 2000: 0.7851294875144958\n",
      "\tMinibatch accuracy: 83.5\n",
      "\tMinibatch loss at epoch 3000: 0.5451874732971191\n",
      "\tMinibatch accuracy: 89.7\n",
      "\tMinibatch loss at epoch 4000: 0.6854653358459473\n",
      "\tMinibatch accuracy: 87.6\n",
      "\tMinibatch loss at epoch 5000: 0.6014248728752136\n",
      "\tMinibatch accuracy: 86.6\n",
      "Test accuracy: 62.7\n",
      "\tMinibatch loss at epoch 6000: 0.5351570248603821\n",
      "\tMinibatch accuracy: 90.7\n",
      "\tMinibatch loss at epoch 7000: 0.43843892216682434\n",
      "\tMinibatch accuracy: 93.8\n",
      "\tMinibatch loss at epoch 8000: 0.5684675574302673\n",
      "\tMinibatch accuracy: 93.8\n",
      "\tMinibatch loss at epoch 9000: 0.4520794451236725\n",
      "\tMinibatch accuracy: 92.8\n",
      "\tMinibatch loss at epoch 10000: 0.5268607139587402\n",
      "\tMinibatch accuracy: 87.6\n",
      "Test accuracy: 63.7\n",
      "\tMinibatch loss at epoch 11000: 0.7904449701309204\n",
      "\tMinibatch accuracy: 90.7\n",
      "\tMinibatch loss at epoch 12000: 0.48936927318573\n",
      "\tMinibatch accuracy: 91.8\n",
      "\tMinibatch loss at epoch 13000: 0.3945162296295166\n",
      "\tMinibatch accuracy: 99.0\n",
      "\tMinibatch loss at epoch 14000: 0.4326641857624054\n",
      "\tMinibatch accuracy: 94.8\n",
      "\tMinibatch loss at epoch 15000: 0.3436991572380066\n",
      "\tMinibatch accuracy: 96.9\n",
      "Test accuracy: 63.4\n",
      "\tMinibatch loss at epoch 16000: 0.4896095097064972\n",
      "\tMinibatch accuracy: 96.9\n",
      "\tMinibatch loss at epoch 17000: 0.39258551597595215\n",
      "\tMinibatch accuracy: 96.9\n",
      "\tMinibatch loss at epoch 18000: 0.4034614861011505\n",
      "\tMinibatch accuracy: 94.8\n",
      "\tMinibatch loss at epoch 19000: 0.3750670850276947\n",
      "\tMinibatch accuracy: 94.8\n",
      "Final Test accuracy: 65.4\n",
      "time taken: 29.0 minutes 49.8 seconds\n"
     ]
    }
   ],
   "source": [
    "Run_Session(20000, 'CNN', .5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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LL7+4h1szmUxITU3FggULzns3PCIKLuPHj0dBQUGdLtwkIkdXmOeffx7Lli3D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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16a6e146c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=[12,7])\n",
    "plt.title('Accuracy for 193Feature set by epoch', fontsize=20)\n",
    "plt.plot(np.array(acc_over_time['RNN'])[:,0], np.array(acc_over_time['RNN'])[:,1], label='RNN')\n",
    "plt.plot(np.array(acc_over_time['DeepNN'])[:,0], np.array(acc_over_time['DeepNN'])[:,1], label='DeepNN')\n",
    "plt.plot(np.array(acc_over_time['CNN'])[:,0], np.array(acc_over_time['CNN'])[:,1], label='CNN')\n",
    "plt.xlabel('Epoch', fontsize=18)\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.ylabel('Accuracy (%)', fontsize=18)\n",
    "plt.ylim([0, 100])\n",
    "plt.legend(loc=\"lower right\", fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dataset_acc = pickle.load(open('dataset_acc.p','rb'))\n",
    "dataset_acc['features193'] = {'DeepNN': np.array(acc_over_time['DeepNN'])[-1,1],\n",
    "                              'RNN': np.array(acc_over_time['RNN'])[-1,1],\n",
    "                              'CNN': np.array(acc_over_time['CNN'])[-1,1]}\n",
    "pickle.dump(dataset_acc, open('dataset_acc.p','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'const_LogMFCC': {'CNN': 39.34707903780069,\n",
       "  'DeepNN': 34.306987399770904,\n",
       "  'RNN': 33.218785796105387},\n",
       " 'features193': {'CNN': 63.45933562428408,\n",
       "  'DeepNN': 62.772050400916378,\n",
       "  'RNN': 54.581901489117982}}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Pvv32W8LCwvzaPR4PL730EvPmzcNkMjFy5EiuvPJKYmJiarMcIYQQQgghhLgo1Wrwa9as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\nRERExM79P/Wf+eYRRe9gAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16a61cce7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotROC(test_labels, test_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# numeralize labels\n",
    "from sklearn import preprocessing\n",
    "le = preprocessing.LabelEncoder()\n",
    "test_batch = le.fit_transform(test['label'])\n",
    "\n",
    "\n",
    "def confusion(true, predicted):\n",
    "    matrix = np.zeros([10,10])\n",
    "    pred = predicted.reshape((predicted.shape[0]//10, 10))\n",
    "\n",
    "    count = 0\n",
    "    for lab in test_batch:\n",
    "        matrix[lab] += pred[count].round()\n",
    "        count += 1\n",
    "        \n",
    "    return matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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M69atSUxMpHv37gQEBNC5c2d2795tw29GRERErOPcWy3ZipJPK+Tk5NC3b1/K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\npPgUQgghhBAG8//3OR21l78emAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c6cc10f9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matrix = confusion(test_batch, test_preds['RNN'])\n",
    "\n",
    "plt.figure(figsize=[10,10])\n",
    "plt.imshow(matrix, cmap='hot', interpolation='nearest',  vmin=0, vmax=200)\n",
    "plt.colorbar()\n",
    "plt.title('RNN Confusion Map', fontsize=18)\n",
    "plt.ylabel('Actual', fontsize=18)\n",
    "plt.xlabel('Predicted', fontsize=18)\n",
    "plt.grid(b=False)\n",
    "plt.yticks(range(10), le.classes_, fontsize=14)\n",
    "plt.xticks(range(10), le.classes_, fontsize=14, rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
